Peer-reviewed research challenges assumptions that students primarily use generative AI to compensate for academic difficulty or time pressure.

PHOENIX, Sept. 18, 2026 /PRNewswire/ — University of Phoenix scholars have published new peer-reviewed research examining how non-traditional undergraduate students use generative artificial intelligence (GenAI) in online higher education. The study found that more frequent GenAI use was significantly associated with higher academic performance and stronger confidence in technology skills, while lower confidence in reading, writing and math and greater time demands from work, study or family responsibilities did not significantly predict use.

The article, “Strategic Integration or Skill Compensation? Understanding GenAI Use in Online Higher Education,” was published June 30, 2026, in Artificial Intelligence in Education, 2(1), 205–223. The research was conducted by fellows and scholars affiliated with the University’s Center for Educational and Instructional Technology Research (CEITR): Jessica Sylvester, EdD; Melinda Kulick, EdD; Leonidas Maganares, EdD; and Stella Smith, PhD, associate university research chair.

Researchers surveyed 491 non-traditional undergraduate students enrolled in online general education courses. Of those surveyed, 43.2%, or 212 students, reported using GenAI tools such as ChatGPT, Microsoft Copilot or Gemini in their coursework.

Sylvester and Kulick previously presented findings from the research at the 2025 Association for Educational Communications and Technology (AECT) International Convention; a summary of that presentation, “Transforming Higher Education: Harnessing Generative AI for Student Engagement,” was subsequently published in The Journal of Applied Instructional Design.

GenAI use linked to academic performance and digital confidence

Multiple regression analysis identified two statistically significant predictors of how frequently students used GenAI:

• Higher GPA: Students with stronger academic performance reported more frequent GenAI use.
• Greater technology confidence: Students who felt more confident in their technology skills also reported using GenAI more frequently.
• Academic confidence: Confidence in reading, writing and math did not significantly predict GenAI use.
• Time demands: Hours spent studying, working or meeting family responsibilities were not significantly related to GenAI use.

Among students who reported using GenAI, 91.1% had a GPA of 3.0 or higher, and 68.9% earned a 4.0 in the course in which they reported using GenAI.

“These findings complicate the assumption that students primarily turn to generative AI because they are struggling academically or simply looking to save time,” said Sylvester, senior manager of College Operations at University of Phoenix and lead author of the study. “Instead, we found GenAI use associated with stronger academic performance and digital confidence. That points to an important opportunity for higher education to help all learners develop the digital fluency, critical judgment and ethical awareness needed to engage with AI effectively.”

Students describe GenAI as a tool for understanding and idea development

Open-ended responses from the 212 students who reported using GenAI provided additional context about how they incorporated the technology into their coursework. The three most prominent themes were:

• improving understanding and clarifying complex concepts
• using GenAI as a “thinking partner” or learning aid for brainstorming and organizing ideas
• increasing efficiency and productivity in academic tasks.

Students also identified concerns about the accuracy and originality of AI-generated content, preservation of their individual academic voice and uncertainty about appropriate use under academic integrity policies.

The researchers suggest the findings support a shift from approaches focused primarily on restricting AI toward clearer guidance that develops GenAI literacy, critical evaluation skills and transparent, ethical use. The study recommends incorporating AI literacy across disciplines, creating assignments that encourage students to critique and reflect on AI-generated information and providing consistent institutional guidance about appropriate use.

Because the research used a cross-sectional, correlational design and a non-probability sample from one online university, the findings do not establish that GenAI use causes stronger academic performance and should not be generalized to all college students. The authors recommend longitudinal and multi-institutional research to examine how patterns of GenAI use evolve and whether AI-supported learning affects longer-term academic outcomes.

Read the full study, “Strategic Integration or Skill Compensation? Understanding GenAI Use in Online Higher Education.” Artificial Intelligence in Education, 2(1), 205–223, at https://doi.org/10.1108/AIIE-08-2025-0237.

About the authors

The authors are affiliates of the Center for Educational and Instructional Technology Research (CEITR), housed within the University of Phoenix College of Doctoral Studies, which supports scholarly research and collaboration on topics related to teaching and learning, online and in-person education, educational technology strategies, and educational policies, practices and theories.

Jessica Sylvester, EdD, is senior manager of College Operations, associate faculty and a research fellow with CEITR. A higher education leader with more than 18 years of experience, her research interests include artificial intelligence, student learning, institutional practices and inclusive learning environments for non-traditional students.

Melinda Kulick, EdD, is assessment manager, associate faculty in the College of Doctoral Studies and a CEITR research fellow at University of Phoenix. She oversees academic assessment for the College of Education and College of Nursing and conducts research exploring generative AI, assessment practices, student engagement and the use of educational technology in online higher education.

Leonidas “Leo” Maganares, EdD, is a research fellow at CEITR and quantitative analysis facilitator with extensive experience in higher education. A former academic affairs leader and dean, he holds a doctorate in Educational Leadership and Change and works as an independent data scientist. His expertise includes quantitative analysis, educational leadership and data science.

Stella Smith, PhD, is associate university research chair for CEITR and associate faculty in the College of Doctoral Studies at University of Phoenix. A qualitative researcher, her scholarly interests include educational access, underserved populations in higher education, leadership, technology and educational pipeline alignment. She earned her PhD in Educational Administration from The University of Texas at Austin.

About the College of Doctoral Studies

University of Phoenix’s College of Doctoral Studies focuses on today’s challenging business and organizational needs, from addressing critical social issues to developing solutions to accelerate community building and industry growth. The College’s research program is built around the Scholar, Practitioner, Leader™ Model which puts students in the center of the Doctoral Education Ecosystem® with experts, resources and tools to help prepare them to be a leader in their organization, industry and community. Through this program, students and researchers work with organizations to conduct research that can be applied in the workplace in real time.

About University of Phoenix 

University of Phoenix is Built for Real Life. 50 Years Strong. The University innovates to help working adults enhance their careers and develop skills in a rapidly changing world through flexible online learning, relevant courses, academic AI pillars, and skills-mapped curriculum for associate, bachelor’s and master’s degree programs. Active students and alumni have access to Career Services for Life^® resources including career guidance and tools. For more information, visit phoenix.edu.

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SOURCE University of Phoenix